Evaluation of compositing algorithms from the regular and non-regular intervals using MODIS daily images in the Amazon region with a high percentage of cloud cover
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Palavras-chave

Time series
Remote sensing
Amazon Forest
Composite image

Como Citar

SALGADO, C. B.; CARVALHO JUNIOR, O. A.; SANTANA, N. C.; GOMES, R. A. T.; GUIMARÃES, R. F.; SILVA, C. R. Evaluation of compositing algorithms from the regular and non-regular intervals using MODIS daily images in the Amazon region with a high percentage of cloud cover. Sociedade & Natureza, [S. l.], v. 34, n. 1, 2022. DOI: 10.14393/SN-v34-2022-65356. Disponível em: https://seer.ufu.br/index.php/sociedadenatureza/article/view/65356. Acesso em: 12 ago. 2022.

Resumo

One challenge in the study of optical remotely sensed time series in the Amazon is the constant cloud cover. The present study evaluates different compositing techniques using regular and non-regular intervals to obtain cloud-free images over large areas. The study area was the municipality of Capixaba, State of Acre, belonging to the Amazon region. The tests considered four compositing algorithms (maximum, minimum, mean, and median) for daily MODIS sensor data (b1 and b2, 250m). The compositing technique from regular intervals adopted the following periods: 8, 16, 24, 32, 40, and 48 days. The irregular interval composite images adopted different composition intervals for dry seasons (April to September) and rainy (October to March). The cloud mask and viewing angle constraint allowed to obtain information without atmospheric interference and closest to the nadir view. The composite images using regular intervals did not allow for overcoming the high frequency of cloud cover in the region. The composite images from non-regular intervals presented a higher percentage of cloud-free pixels. The mean and median methods provided a better visual appearance of the images, corroborating with the homogeneity test. Therefore, composite images from non-regular intervals may be an appropriate alternative in places with constant cloud coverage.

https://doi.org/10.14393/SN-v34-2022-65356
PDF-en (English)

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Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.

Copyright (c) 2021 Cristiane Batista Salgado, Osmar Abílio Carvalho Junior, Nickolas Castro Santana, Roberto Arnaldo Trancoso Gomes, Renato Fontes Guimarães, Cristiano Rosa Silva

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